The focus of this post is sentiment analysis. This is a Natural Language Processing (NLP) application I find challenging but enjoyable. It aims at identifying emotional states, reactions and subjective information. It tries to determine the attitude of a speaker with respect to some topic. If done automatically with high precision and on a large scale, sentiment analysis could be a goldmine for marketers or politicians who want to measure the public opinion through social networks. In this post I'll show you how I built a machine learning model that classifies tweets with respect to their polarity. Tweets are short and yet capture lots of subjective information. That's why we'll be playing with them. Some words for those who are ready to dive in the code: I'll be using python, gensim, the word2vec model and Keras.

In this tutorial we'll dive in Topic Mining. We'll analyze a dataset of newsfeed extracted from more than 60 sources. We'll show how to process it, analyze it and extract visual clusters from it. We'll be using great python tools for interactive visualization, topic mining and text analytics. All the code is available to you to run and test. No bullshit.